Many modern consumer electronics products have the capability to capture and process image data. For example, laptop computers, tablet computers, smartphones and personal media devices may have cameras to capture image data and image editing applications that process the image data. Image editing applications may provide tools to crop and/or rotate image content and also to alter image content, for example, by altering image brightness, color content, sharpness and the like.
Some image editing applications attempt to alter image characteristics autonomously thereby relieving human operators from the burden of selecting and applying image editing tools. One such automated operation involves filtering. An image editing application may attempt to identify which portions are to be filtered based on assessments of the portions' characteristics. Automated analysis tools, however, sometimes develop “false positives.” For example, an analysis tool may assign a first portion of an image for relatively heavy filtering but a second portion of an image for little or no filtering. When these filtered and unfiltered portions actually belong to a common element of an image, such as a human face, these differences in filtering lead to undesirable artifacts.